abstract = "Animals such as bees, ants, birds, fish, and others
are able to perform complex coordinated tasks like
foraging, nest-selection, flocking and escaping
predators efficiently without centralized control or
coordination. Conventionally, mimicking these
behaviours with robots requires researchers to study
actual behaviors, derive mathematical models, and
implement these models as algorithms. We propose a
distributed algorithm, Grammatical Evolution algorithm
for Evolution of Swarm behaviours (GEESE), which uses
genetic methods to generate collective behaviors for
robot swarms. GEESE uses grammatical evolution to
evolve a primitive set of human-provided rules into
productive individual behaviors. The GEESE algorithm is
evaluated in two different ways. First, GEESE is
compared to state-of-the-art genetic algorithms on the
canonical Santa Fe Trail problem. Results show that
GEESE outperforms the state-of-the-art by (a) providing
better solution quality given sufficient population
size while (b) using fewer evolutionary steps. Second,
GEESE outperforms both a hand-coded and a Grammatical
Evolution-generated solution on a collective swarm
foraging task.",

notes = "Also known as \cite{3205619} GECCO-2018 A
Recombination of the 27th International Conference on
Genetic Algorithms (ICGA-2018) and the 23rd Annual
Genetic Programming Conference (GP-2018)",